packages = c('raster','sf','tmap','tidyverse','clock','rgdal','tidytext','widyr',
'DT','dplyr','hms','ggraph','igraph','crosstalk',
'plotly','data.table','stringi','mapview','ggridges','networkD3',
'htmlwidgets')
for(p in packages){
if(!require(p, character.only = T)){
install.packages(p)
}
library(p, character.only = T)
}
Loading data
cc <- read_csv(file = 'data/cc_data.csv')
lc <- read_csv(file = 'data/loyalty_data.csv')
bgmap <- raster("data/MC2-tourist.tif")
Abila_st <- st_read(dsn = "data/Geospatial",
layer = "Abila")
Reading layer `Abila' from data source
`C:\abmayur05\VisualAnalytics\_posts\2021-07-26-vast-challenge-mc2-part-2\data\Geospatial'
using driver `ESRI Shapefile'
Simple feature collection with 3290 features and 9 fields
Geometry type: LINESTRING
Dimension: XY
Bounding box: xmin: 24.82401 ymin: 36.04502 xmax: 24.90997 ymax: 36.09492
Geodetic CRS: WGS 84gps <- read_csv("data/gps.csv")
net <- read_csv(file = 'data/car-assignments.csv')
Task 2
Add the vehicle data to your analysis of the credit and loyalty card data. How does your assessment of the anomalies in question 1 change based on this new data? What discrepancies between vehicle, credit, and loyalty card data do you find? Please limit your answer to 8 images and 500 words.
Plotting Raster Layer
tm_shape(bgmap) +
tm_rgb(bgmap, r = 1, g = 2, b = 3,
alpha = NA,
saturation = 1,
interpolate = TRUE,
max.value = 255)
The map of Abila gives us a clear picture of the distance that GASTech is away from the other locations visited by the employees. From the map of Abila, the most frequented locations such as Katerina’s Cafe, Guy’s Gyros, Brew’ve Been Served were all seems to be in close proximity with GASTech. Hippokampos was the second most frequented location but surprisingly it was not present in the map. Since the top 4 frequented locations are closely located, I assume Hippokampos also should be near to GASTech.
Converting Date-Time field and ID field
gps$Timestamp <- date_time_parse(gps$Timestamp,
zone = "",
format = "%m/%d/%Y %H:%M:%S")
gps$id <- as_factor(gps$id)
Converting Aspatial Data Into A Simple Feature data Frame
gps_sf <- st_as_sf(gps,
coords = c("long","lat"),
crs = 4326)
gps_sf <- gps_sf %>%
mutate(day = get_day(Timestamp),
hour = get_hour(Timestamp),
dayofweek = date_weekday_factor(Timestamp) ,
date = as_date(Timestamp))
glimpse(gps_sf)
Rows: 685,169
Columns: 7
$ Timestamp <dttm> 2014-01-06 06:28:01, 2014-01-06 06:28:01, 2014-01~
$ id <fct> 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 35~
$ geometry <POINT [°]> POINT (24.87469 36.07623), POINT (24.8746 36~
$ day <int> 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,~
$ hour <int> 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,~
$ dayofweek <ord> Mon, Mon, Mon, Mon, Mon, Mon, Mon, Mon, Mon, Mon, ~
$ date <date> 2014-01-06, 2014-01-06, 2014-01-06, 2014-01-06, 2~Creating Movement Path From GPS Points
The code chunk below joins the GPS points into movement paths by using car id, date and hour as unique identifiers.
gps_path <- gps_sf %>%
group_by(id, date, hour) %>%
summarize(timestamp= mean(Timestamp),
do_union=FALSE) %>%
st_cast("LINESTRING")
Finding the orphan lines
After getting the movement paths we do see some orphan GPS points hence finding those orphan lines.
p = npts(gps_path, by_feature = TRUE)
gps_path2 <- cbind(gps_path, p)
Removing the orphan lines
gps_path3 <- gps_path2[!(gps_path2$p==1),]
Plotting the GPS Paths
Below code chunk is used to overplot the GPS path of all the car ids onto the background tourist map.
gps_path_selected <- gps_path3
tmap_mode("view")
tm_shape(bgmap) +
tm_rgb(bgmap, r = 1, g = 2, b = 3,
alpha = NA,
saturation = 1,
interpolate = TRUE,
max.value = 255) +
tm_shape(gps_path_selected) +
tm_lines(col='red')